Music identification based on music word model

For music identification, conventional bag of audio words model methods generally compute a histogram for a piece of music, which ignores the temporal characteristic of music and has a negative influence on the accuracy. In addition, they are usually based on DFT spectrogram, which cannot represent music as well as Constant Q (CQ) spectrogram. To address the above problems, we propose a two-layer representation method based on a set of music words for music identification. Firstly, music words are learned from the CQ spectrogram as typical patterns. Then, based on the obtained music words, a piece of music can be represented as word sequence and word histogram. We can reduce the number of possible similar candidates effectively with the histogram similarity measure, and the final result is determined by the sequence similarity measure. Based on the distribution of music word frequency, a low frequency word filter strategy is devised to increase the identification speed, which is essential for large systems such as a million song library. Experiments demonstrate the effectiveness and efficiency of our proposed method.